Suparse Document Processing

Suparse MCP Server

MCP stdio server for the Suparse Document Processing API. Use it from local MCP clients such as Claude Code and Codex to extract structured data from documents into JSON, CSV, XLSX, or Google Sheets; process single files or folders, including encrypted PDFs; let Suparse auto-detect extraction schemas or apply your team templates; create schemas automatically; split mixed multi-page documents; and download or clean up results by document ID.

Security Boundary

This is a local stdio MCP server. Connected MCP clients can ask it to read local document paths and write export files wherever the server process has permission. Only connect it to MCP clients and workspaces you trust.

Requirements

• A Suparse API key
• Node.js 20+

Authentication

The MCP server reads credentials from:

1. SUPARSE_API_KEY
2. ~/.config/suparse/config.json

Prefer passing SUPARSE_API_KEY through your client's environment or secret-input support. The fallback file is implemented by Suparse and contains { "apiKey": "your_api_key" }. Protect it as a credential file.

Replace your_api_key in the examples below. Client env settings and CLI --env options can save the key in client configuration; keep files containing real keys out of Git and shared workspaces. CLI commands containing a key may also appear in shell history. Use scoped API keys where available.

Local Installation

Install your chosen client first and ensure Node.js 20+ and npx are available to it. These setups launch @suparse/mcp as a local stdio process:

• Claude Code
• Claude Desktop
• Codex CLI / IDE
• Cursor
• VS Code / GitHub Copilot
• Gemini CLI
• Windsurf / Cascade

Merge JSON examples into existing configuration rather than replacing other servers. After connecting, use the template-listing check to verify Suparse authentication.

Claude Code

Install for all your projects with user scope:

claude mcp add --scope user --transport stdio suparse \  --env SUPARSE_API_KEY=your_api_key \  -- npx -y @suparse/mcp

Keep the server name (suparse) before --env; Claude Code can otherwise parse it as another environment variable and report Invalid environment variable format.

To change an existing registration (for example, its API key or launch command), remove it before running the add command again:

claude mcp remove --scope user suparse

Use the name and scope from your original installation. For a user-scoped local test registered as suparse-local, run claude mcp remove --scope user suparse-local. After adding the updated registration, fully exit Claude Code and start a fresh session to load the new configuration.

Check the configuration and connection:

claude mcp listclaude mcp get suparse

Inside Claude Code, use /mcp to inspect status and tools, then run the template-listing check. See the Claude Code MCP documentation.

Claude Desktop

Open your config file at ~/Library/Application Support/Claude/claude_desktop_config.json on macOS or %APPDATA%\Claude\claude_desktop_config.json on Windows. Add Suparse to the mcpServers section:

{  "mcpServers": {    "suparse": {      "command": "npx",      "args": ["-y", "@suparse/mcp"],      "env": {        "SUPARSE_API_KEY": "your_api_key"      }    }  }}

Fully quit and reopen Claude Desktop, confirm Suparse appears among the available tools, then run the template-listing check.

If Windows reports a process-start error for npx, replace the server's command and args with:

{  "command": "cmd",  "args": ["/c", "npx", "-y", "@suparse/mcp"]}

Keep the existing env block. This workaround is only needed for clients that cannot launch npx.cmd directly.

Codex

The preferred installation is through the CLI:

codex mcp add suparse \  --env SUPARSE_API_KEY=your_api_key \  -- npx -y @suparse/mcp

After installation, add tool_timeout_sec = 300 inside the existing [mcp_servers.suparse] table in ~/.codex/config.toml. The CLI command does not set this timeout.

Alternatively, configure the server manually in ~/.codex/config.toml or a project-scoped .codex/config.toml:

[mcp_servers.suparse]command = "npx"args = ["-y", "@suparse/mcp"]tool_timeout_sec = 300
[mcp_servers.suparse.env]SUPARSE_API_KEY = "your_api_key"

tool_timeout_sec gives Suparse extraction tools up to five minutes to finish; Codex defaults to 60 seconds. The CLI and IDE extension share this configuration. See the Codex MCP documentation.

Verify the configured server:

codex mcp list

In an interactive Codex session, use /mcp to inspect active servers, then run the template-listing check.

Cursor

Add this to ~/.cursor/mcp.json for all projects, or .cursor/mcp.json for the current project:

{  "mcpServers": {    "suparse": {      "type": "stdio",      "command": "npx",      "args": ["-y", "@suparse/mcp"],      "env": {        "SUPARSE_API_KEY": "${env:SUPARSE_API_KEY}"      }    }  }}

Set SUPARSE_API_KEY in Cursor's environment before launching it; the ${env:SUPARSE_API_KEY} reference keeps the key out of the JSON file. Restart Cursor after changing its environment. Alternatively, put the key in an untracked personal configuration.

Open Cursor's MCP settings, confirm Suparse is enabled and its tools are available, then run the template-listing check in Agent chat. See the Cursor MCP documentation.

VS Code / GitHub Copilot

For Copilot CLI, install and verify with:

copilot mcp add suparse \  --env SUPARSE_API_KEY=your_api_key \  --timeout 300000 \  -- npx -y @suparse/mcpcopilot mcp list

The timeout is in milliseconds and allows up to five minutes for tool discovery and calls. Then run the template-listing check in Copilot CLI. See the Copilot CLI command reference.

For a portable Copilot setup, create .mcp.json in the workspace root. For a personal installation, use ~/.copilot/mcp-config.json (or $COPILOT_HOME/mcp-config.json if configured):

{  "mcpServers": {    "suparse": {      "type": "stdio",      "command": "npx",      "args": ["-y", "@suparse/mcp"],      "env": {        "SUPARSE_API_KEY": "your_api_key"      },      "tools": ["*"]    }  }}

Keep any file with a real key untracked. For a workspace configuration using VS Code's password input, use .vscode/mcp.json instead:

{  "inputs": [    {      "id": "suparse-api-key",      "type": "promptString",      "description": "Suparse API key",      "password": true    }  ],  "servers": {    "suparse": {      "type": "stdio",      "command": "npx",      "args": ["-y", "@suparse/mcp"],      "env": {        "SUPARSE_API_KEY": "${input:suparse-api-key}"      }    }  }}

The VS Code format uses servers; the portable format uses mcpServers. Choose one setup to avoid duplicate servers. Interactive inputs are not forwarded to Copilot harness sessions; use the portable setup for those sessions.

Run MCP: List Servers from the Command Palette, select Suparse and start it, then run the template-listing check in agent chat. See the VS Code MCP configuration reference.

Gemini CLI

Install for your user account:

gemini mcp add --scope user \  --env SUPARSE_API_KEY=your_api_key \  suparse npx -y @suparse/mcp

This writes the server configuration to ~/.gemini/settings.json. Check connection status with:

gemini mcp list

In an interactive session, use /mcp list to inspect tools, then run the template-listing check. See the Gemini CLI MCP documentation.

Windsurf / Cascade

These instructions target the legacy Cascade agent. For the newer default Devin Local agent, follow the Devin CLI MCP configuration documentation; its configuration locations and migration behavior depend on the installed version, even where paths overlap with Cascade.

Open the MCP configuration from Cascade's MCP controls. Windsurf installations use ~/.codeium/windsurf/mcp_config.json (%USERPROFILE%\.codeium\windsurf\mcp_config.json on Windows). Cascade in current Devin Desktop releases uses ~/.config/devin/mcp_config.json (or $XDG_CONFIG_HOME/devin/mcp_config.json) on macOS/Linux and %APPDATA%\devin\mcp_config.json on Windows. Opening the file from Cascade selects the path for your installed version.

Add Suparse under mcpServers:

{  "mcpServers": {    "suparse": {      "command": "npx",      "args": ["-y", "@suparse/mcp"],      "env": {        "SUPARSE_API_KEY": "your_api_key"      }    }  }}

Save the file, refresh the MCP list or restart the client, and confirm Suparse and its tools are enabled in Cascade. Run the template-listing check. See the Cascade MCP documentation.

Verify Your Installation

In your client's agent chat, ask:

Use Suparse's list_templates tool to list my team's available extraction templates.

Approve the tool call if prompted. A successful response confirms both the MCP connection and Suparse API authentication; an empty team_templates list is valid if your team has no templates. This check does not upload or process a document.

If the server fails to start, check that Node.js 20+ and npx are on the client's PATH, and inspect its MCP logs. For authentication errors, check the key passed to the server or the Suparse fallback file. Desktop applications may not inherit environment variables set in a terminal.

Verify Document Processing (Optional)

Processing may consume Suparse credits. Use a small sample invoice or receipt containing data you are comfortable uploading to Suparse.

1. Put the sample PDF somewhere accessible to the MCP server and use its absolute path (see local paths).
2. Ask your client: "Use Suparse's start_file_extraction to extract structured data from /absolute/path/to/sample.pdf. Return the task ID and any document IDs or processing status provided. Do not delete the uploaded document."
3. Call wait_for_task_extraction with the returned task ID. If processing is still running, retain the ID and poll again; do not upload the file again.
4. Once processing completes, use a returned document ID to call fetch_document_result. If necessary, use get_task_results with the task ID to discover document IDs.

This checks local file access, authentication, upload, processing, and result retrieval. Keep the uploaded document unless you explicitly want to delete it later.

Tools

defer uploads and polls until processing reaches a terminal state or polling fails; it defers the result export, not the processing wait. The start-only tools return task IDs after upload confirmation without polling for processing completion. Use them when you need an ID promptly, especially for long jobs or human review.

return_json returns full results in both text and structured content. Large documents or batches can consume substantial assistant context; prefer download_results to save exports for those jobs.

Local Paths

Input and output paths must be accessible to the machine and operating-system process running Suparse MCP. Relative paths such as ./invoice.pdf resolve against the server process's current working directory, which the MCP client chooses at launch; this may differ from your open project or chat's directory. File paths pass directly to the Node SDK's filesystem operations, and folder paths are normalized with path.resolve against that same working directory. Relative export paths also use the process's working directory; omitted output paths default there. The server does not expand ~ in tool path arguments.

Prefer absolute paths when the working directory is uncertain. Use paths for the server's operating system: for example, C:\Users\you\invoice.pdf for a Windows process or /mnt/c/Users/you/invoice.pdf for a WSL2 process with that drive mounted. A path on another machine or outside the server's accessible filesystem cannot be read just because it appears in chat.

Credits, Deletion, and Retries

Document processing and automatic schema creation may consume Suparse credits. Uploading the same file again can create another processing submission. If a request times out, recover using the retained task ID, export job ID, or idempotency key before submitting new work.

delete_documents deletes documents from Suparse remotely; it does not just remove local temporary files. The cleanup option on combined extraction tools can also delete remote documents after a verified export. Request deletion only after confirming that the results you need are safely saved.

The server already supplies MCP annotations: status, template, and result inspection tools are read-only; upload/schema-creation tools are mutating and non-idempotent; combined extraction is marked potentially destructive because it can clean up; and delete_documents is marked destructive. Export submission tools are not advertised as universally idempotent: safe replay requires the same key and identical request options. Disk export tools can also write or overwrite local files. These hints help clients assess tool calls, but do not enforce permissions or make arbitrary retries safe.

Tool Reference

• extract_file, extract_folder: Combined upload and processing. defer is the default and returns task/document IDs after polling; a task can be partially_completed. return_json returns exported groups with per-input task status and document IDs; partial tasks and empty exports are reported as incomplete. cleanup is best-effort and valid only with return_json.
• start_file_extraction, start_folder_extraction: Upload and return task IDs immediately, without polling or exporting. Folder scans are sorted, nonrecursive, and limited to 10 concurrent uploads.
• wait_for_task_extraction: Poll a remote task. Defaults to 2 seconds between polls and 3 attempts; retries and SDK backoff mean this is not a strict wall-clock deadline.
• get_task_results: Read one bounded page of partial task documents. The opaque cursor ends a snapshot, not task processing; a fresh call may show more documents.
• list_templates: List summary or full template records, grouped into directly usable team_templates and discovery-only system_templates.
• start_quick_schema_creation, get_quick_schema_creation, wait_for_quick_schema_creation: Start and inspect automatic schema creation. The run ID is separate from task, document, and export job IDs.
• fetch_json_results: Return validated TaskExport[] JSON by document ID. It does not unpack ZIP artifacts or accept arbitrary JSON schemas.
• fetch_document_result: Fetch one document's structured JSON result through the direct single-document endpoint.
• download_results: Export documents and save the response to a local path. Supports JSON, CSV, XLSX, and immediate Google Sheets. Existing output paths follow the SDK overwrite behavior.
• get_export_job, wait_for_export_job, download_export_job: Inspect, wait for, and download a known remote export job across MCP restarts. Downloads preserve original bytes, including ZIP, and verify the artifact before atomic publication. download_export_job defaults to overwrite: false.
• delete_documents: Delete remote documents by ID. Use after confirming a successful export/download when cleanup is requested.

Export Formats

fetch_json_results accepts:

JSON exports are returned as structured results. fetch_json_results and download_results accept wait_timeout_ms (positive integer milliseconds, default 60000) and an optional UUID idempotency_key. The wait value bounds completion waiting, including polling retries; submission and transfer are separate phases, so MCP clients still need enough total tool time. If a response is lost, replay the same document IDs, format, export type, layout, and key.

download_results accepts json, csv, xlsx, and google_sheets, plus an optional output_path local file path or existing directory. XLSX exports additionally accept xlsx_layout: "standard" | "flat" and default to standard. It writes the export directly to disk and returns the saved output_path. Immediate Google Sheets responses work; background Google Sheets is unsupported. The disk tool saves the Sheets response metadata, including URLs, rather than a spreadsheet binary. MCP clients should use download_results for CSV, XLSX, Google Sheets, and saved JSON files; they should not fetch base64 data and decode it with shell or Python. A known-job download preserves raw artifact bytes and is the path for ZIP or exact JSON artifacts.

CSV/XLSX exports can return a ZIP containing several files (for example, invoices and bank statements). Both download tools report requested_format, artifact_format, and content_type, and correct the saved extension to match the artifact. An explicit results.xlsx path therefore becomes results.zip when the API returns an archive; check the returned output_path.

For separate local files, call download_results with unpack: true:

{"document_ids":["invoice-id","statement-id"],"format":"xlsx","output_path":"/absolute/path/results.xlsx","unpack":true}

The ZIP is retained, and output_paths lists the extracted files in a fresh results_files-* folder next to it. Existing extracted files are never replaced. A single workbook stays .xlsx and appears directly in output_paths. Unpacking defaults to false, applies only to CSV/XLSX, and rejects unsafe archive paths, links, duplicate destinations, more than 1000 entries, or more than 512 MiB of uncompressed data. Failed extraction removes partial files; error recovery includes saved_artifact_path if the archive was already saved. download_export_job always preserves the original artifact without unpacking. CSV/XLSX download_results buffers the artifact through the SDK's raw-download API; known-job recovery streams and verifies the artifact before publication.

result_mode controls whether extraction results in JSON format are returned directly. Use return_json only when you need the full JSON extraction in the MCP response. In all other cases you can retrieve the results in the format of your choice using download_results.

Explicit return_json requests return complete JSON in both text and structured content, including when cleanup is enabled. Its tasks records preserve each polled input’s task ID, status, and document IDs. Partial completion and empty exports increase failed_count while preserving any exported data; their parent tasks are retained during cleanup. Combined extraction accepts wait_timeout_ms for each JSON export, defaulting to 60000 milliseconds; it is ignored in defer mode. Its optional exports records associate generated idempotency keys and accepted job IDs with each local input. Combined extraction does not accept caller-supplied idempotency keys.

Cancelled folder responses account for all scanned files. Inputs that never started appear in failed with cancelled: true, unstarted: true, and code OPERATION_NOT_STARTED; failed_count includes them. These entries remain in source order and distinguish inputs safe to start from submitted tasks needing recovery. A known export job takes recovery priority, followed by replaying an identical export with its retained key, then task polling.

Important: cleanup on extract_file and extract_folder is only valid with result_mode: "return_json". Cleanup is best-effort and requires a fully completed task and a JSON export covering every requested document ID. Missing or unverifiable coverage produces an INCOMPLETE_EXPORT failure and retains the parent. It retains partial tasks, empty exports, and inputs whose backend work is uncertain. Folder cleanup waits for all extraction workers to settle and is skipped after cancellation. For CSV/XLSX/Google Sheets or saved JSON files, use start or combined extraction, wait for the task, download the result, then call delete_documents if requested. A local artifact write failure alone does not prove backend work is still running. When cleanup is requested, cleanup reports its status and deleted or retained document IDs in both text and structured results. Retained IDs include deletions that could not be confirmed. Combined extraction progress counts settled input files and increases with every notification.

extract_file, extract_folder, start_file_extraction, and start_folder_extraction accept template_id, split, pdf_password, folder_id, and auto_approve. Approval defaults to true. Set auto_approve: false only when human review is requested, and use the start-only tools so the task ID is returned before processing waits. MCP cannot perform human review or approve documents; retain the task ID and resume polling after review is complete. This does not create or report a backend awaiting_approval status. Treat pdf_password as sensitive input. MCP redacts it from returned errors, metadata, partial results, and progress, while an MCP client may retain submitted tool arguments according to its own behavior.

Google Sheets Requirements and Results

• Connect Google Sheets in Suparse before requesting format: "google_sheets"; the SDK requires a working Google integration.
• The Suparse integration supports creating a spreadsheet or appending to an existing one. This MCP tool exposes no target spreadsheet ID or append option, and the SDK does not specify which destination behavior its export endpoint chooses. Selecting a destination or explicitly appending rows is not supported through this MCP server.
• Only immediate Google Sheets export responses are supported here; background Google Sheets jobs cannot be downloaded through this workflow.
• download_results saves a JSON metadata file and returns output_path plus available google_sheets_metadata, which may include spreadsheet or folder URLs. It does not save a local .xlsx file. Use format: "xlsx" for that.

Recovery guide

Typical workflows

Ordinary extraction and download:

extract_file(file_path="./invoice.pdf")download_results(document_ids=["<document-id>"], format="csv", output_path="./exports")

Long-running work and partial inspection:

start_file_extraction(file_path="./invoice.pdf")wait_for_task_extraction(task_id="<task-id>", poll_interval=2, max_poll_attempts=3)get_task_results(task_id="<task-id>", limit=25, cursor=null)

poll_interval is seconds. Export wait_timeout_ms is milliseconds. A task may be partially_completed; retain its completed document IDs and inspect later pages. When a page returns next_cursor: null, that snapshot is exhausted but the task may still produce documents. Start a fresh page request later and track IDs to avoid duplicates.

Human review:

start_file_extraction(file_path="./contract.pdf", auto_approve=false)

The returned task_id is a remote task ID, document_ids identify remote documents, run_id belongs to quick-schema creation, export_job_id identifies a remote export job, and output_path is a local filesystem path.

Recover an accepted export after a timeout or lost response:

get_export_job(export_job_id="<export-job-id>")wait_for_export_job(export_job_id="<export-job-id>", wait_timeout_ms=60000)download_export_job(export_job_id="<export-job-id>", output_path="./exports/result.zip")

The 60000 millisecond SDK wait bound includes polling retries, but not submission or artifact transfer. Set a suitable timeout in the MCP client for the full operation. Parsed JSON tools return validated TaskExport[]; known-job downloads keep the raw artifact bytes, which may be ZIP or exact JSON. Do not expose base64/raw buffers to chat or decode artifacts with shell commands.

Automatic Schema Creation

Call start_quick_schema_creation with a local file, then pass its normalized run_id to get_quick_schema_creation or wait_for_quick_schema_creation. Terminal results preserve generated template IDs, saved documents, failed-document details, and retryability. The start tool also accepts pdf_password and folder_id.

upload_batch_id and source_upload_id are supported only for advanced workflows that obtain those IDs elsewhere; the MCP server does not create upload batches.

Template Selection for MCP Agents

MCP agents should use only team_templates when passing template_id to an extraction tool. Use the default summary view for normal selection and request view: "full" only when schema, prompt, validation, or creator metadata is needed.

When a user asks to process a document type such as a receipt:

1. Check team_templates first and use the matching team template if present.
2. If no matching team template exists, call list_templates with include_system: true and check system_templates.
3. System templates are discovery-only. MCP cannot add them to the team or use their IDs directly for extraction.
4. If no matching team template exists, omit template_id for auto-detection, or use start_quick_schema_creation and follow its run_id when the user authorized schema creation. Custom template editing and manual configuration are not exposed through MCP; explain that limitation if requested.

Development

Build the package:

pnpm build

Test with MCP Inspector:

npx @modelcontextprotocol/inspector node dist/index.mjs

The MCP server uses stdout for JSON-RPC protocol messages. Do not add console.log output to the server path; use stderr or MCP tool responses.